A processor, a system on a chip (SoC), and an application specific integrated circuit (ASIC) can include multiple cores for performing compute operations such as processing digital signals, performing cryptography, executing software applications, rendering graphics, and the like. Multiple cores can be implemented as vector processors using the principle of single instruction multiple data (SIMD). In SIMD, multiple processors perform an operation simultaneously on multiple data points (e.g., as a set of data samples, also referred to as elements). Thus, SIMD exploits data level parallelism for multiple simultaneous computations for a single process or instruction.
One example of a process that can be performed by multiple cores using SIMD is peak detection. Peak detection is the problem of finding local maxima points on a set of data samples (also referred to as elements). Samples can be compared by comparing the magnitude or the squared magnitude (e.g., the power) of the sample with the magnitude or power of nearby samples by scanning through the data. Thus, for a stream of incoming data x, the point xn is defined as a local maximum if it satisfies the following:|Xn−1|<|Xn|>|Xn+1|
Peak detection can be used for signal processing functions such as peak cancellation based crest factor reduction (PC-CFR). In PC-CFR, detected peaks are qualified to check whether they exceed a preset threshold. The qualification chooses the highest peaks over a predefined block of data called the peak detect window (PDW). The qualified peaks are scaled, filtered, and then subtracted from the original data stream. Thus, with PC-CFR using peak detection the resultant signal shows a lower peak-to-average ratio.
Peak detection involves the same operation to be performed on the set of data samples. The peak detection involves element-wise comparison and index finding, which makes it difficult for implementation on a vector processor. For example, peak detection typically involves breaking the vector pipeline or resorting to a scalar operation over a large set of data. This results in additional compute cycles and slows the process.
Thus, improved techniques for peak detection, for example for PC-CFR, would be useful.